Many businesses use a campaign process to deliver marketing offers to a variety of consumers. The campaign process may be, for example, by telephone or by mass mailing. In order to define the campaigns to execute, the business may gather and aggregate information about their customers from a variety of data sources, both from within their company as well as from third party data providers. After gathering the consumer information, the businesses may decide to separate customers into groupings, customer segments, which have similar characteristics. The businesses may then create a specific list of consumers that the businesses hope will respond positively to the campaign. Sometimes, these lists may be produced using generalized marketing response models—models developed on generalities about the firm's customers rather than specifics about likely customer response to forthcoming campaign offers. These general models are sub-optimal. But more often, the lists are purchased from third-party vendors, or extracted from internal databases using SQL-based rules. Not infrequently, telemarketing relies simply on lists of bare telephone numbers selected from particular area codes and exchanges, with no information about the prospect until the contact is actually established.
This process typically can be time consuming and deliver sub-optimal results. Businesses typically employ personnel to search for the consumer information. The personnel may individually search a number of disparate databases attempting to gather the consumer information. This could include information that helps to identify the customer (e.g., name, address, phone, electronic mail address, etc.), information on products or services the customer has purchased in the past, and any additional contextual information captured during past contacts with the customer. Oftentimes, this information is stored in disparate databases in inconsistent formats, making it very difficult to formulate a total, integrated view of a customer. The databases may also contain stale data that produces poor or even erroneous results.
Businesses may attempt to purchase additional information about existing or prospective customers from third party data providers (e.g., Equifax, etc.). Types of information purchased may include demographic data (e.g., income level, house size), lifestyle data (e.g., activities the customer participates in, etc.), and interests (e.g., information indicating the customer enjoys eating at restaurants, going to see movies, etc.). Oftentimes, businesses find it challenging to integrate externally purchased data with their own customer data. When data is merged from multiple data sources, sophisticated programming skills are required to link records as well as to aggregate information and calculate values that could be useful to predict customer behavior. Further, the extraction of data from multiple sources to drive analytical modeling can be a very laborious, time consuming process given the number of joins that have to be written. Oftentimes, businesses do not have common extract procedures meaning that new extract routines have to be written each time a new form of data analysis needs to be performed.
More advanced database marketers make heavy use of analytics and modeling. Customer segmentations based on commercially available demographics, lifestyle, and life-stage data are often used to help define campaigns. These data are also used to target individuals. Unfortunately, because these data are usually compiled at the zip code or census-tract level, application to individuals for targeting is subject to a great deal of error. Propensity models (models comparing attributes of prospect lists to attributes of existing customers) are often developed by businesses and used to develop targeting lists of persons who look like existing customers, hence may have a greater propensity to respond to the business' marketing campaigns. Some more sophisticated businesses are able to develop response models (models based on respondents to actual campaigns); these models tend to outperform the other list generating methods. However, these more sophisticated models require more sophisticated methods and better data. The cost of developing these models can be high.
For example, a typical model development process may require two or three people and four to twelve weeks (i.e., 12-36 people-weeks) to extract the required customer data and build an analytic model. Then developing a scoring algorithm may take a person four additional weeks. Thus, targeting models are costly. The cost and time required for model development encourages the development of generalized marketing models that are often used for a year or more. Generalized models are commonly outperformed by as much as one hundred percent (100%) by models developed specifically for a particular campaign or offer. Over time, models degrade in performance, but are often used long after their performance peak. This results in diminished marketing returns and often results in abandonment of the use of models for targeting. A second problem is that the data used to create the predictive models and ultimately define and execute the marketing campaigns is old by the time the models are run, leading to out of date model results and poor offer acceptance rates for the resulting marketing campaigns.
The time-consuming conventional modeling and marketing processes cannot support rapid test and learn iterations that could ultimately improve offer acceptance rates. After completing a marketing campaign, the personnel may gather the results of the campaign to determine a success rate for the campaign. The results, however, are typically not effectively fed back into the customer information database and used to re-analyze predictive customer behavior. Without an effective closed-loop, businesses lose the ability to retrain their analytical models and improve their campaigns by defining campaigns that have a greater return.
The effect of the previously described issues extend beyond marketing campaigns to all forms of interaction. A business' inability to execute an effective, closed loop process to tailor their marketing campaigns affects all forms of customer interaction. Ideally, a business should strive to deliver the right message to the right customer through the best channel. Customers who are the target of an outbound marketing campaign should be able to receive the same offer should they interact with the business through any interaction channel (e.g., web, phone, retail branch, etc.) to perform a service transaction, sales transaction, etc. However, since traditional methods prevent the business from quickly generating reliable, targeted offers for customers based upon predictive analytical models and refined through rapid test and learn iterations, they are unable to deliver optimized marketing offers tailored to their customers and prospects across all forms of customer interaction; best offer to the right customer through the best channel.